International Journal of Science, Engineering and TechnologyAuthors: Guni Paliwal, Mohini, Yashvi Gaur, Dr. Lalit Kumar Sagar
Abstract: Depression is one of the most common mental health problems, but it often goes unnoticed due to the social shame associated with the mental health issues and because employees are not regularly monitored for their psychological well-being. The recent advances in Natural Language Processing are now able to identify the emotional patterns from written text. This offers a way of tracking the changes in emotional and mental health in a way that doesn’t make the person feel watched or monitored, thus protecting their privacy. Here, we propose a method to analyze diary-style entries written by employees, which can show early signs of depression. It is a quiet way to check on people’s mental health as this system uses things that have already written, like journals, rather than asking the direct and personal questions. In this approach, we used two complementary models: LSTM network which captures that how emotions appear in the text as they change over time, and a fine-tuned BERT model which understands the deeper and more complex meaning and context of the words used. The text data goes through structured preprocessing: normalization, tokenization, and addressing class imbalance. For analysis, the text is categorized into three levels of depression: no depression, moderate depression, and severe depression. The system is evaluated based on its performance in identifying the depression, and is done by evaluating precision, recall, and F1-score, but the main focus is on recall which makes sure that the system doesn’t miss anyone who might need help. This is done to be extra careful and to avoid overlooking the employees who might be at risk. For clarity, the system uses two main techniques to explain its decisions, one is attention-based highlighting, which highlights the most important words or sentences that influenced the analysis. The other technique, called SHAP analysis, which helps to drive the model’s decisions by showing that how each part of information in text contributed to final conclusion. To protect employee privacy, the system uses ethical safeguards which make sure the data is anonymous, getting permission from user before analysis. The entire system is designed to be the most supportive tool for companies to detect early signs of stress and to create a more caring and healthy workplace for everyone.
DOI: http://doi.org/10.5281/zenodo.21786596
